The outbreak of COVID-19 had already shown its harmful impact on mankind, especially on health sectors, global economy, education systems, cultures, politics, and other important fields. Like most of the affected countries in the globe, India is now facing serious crisis due to COVID-19 in the recent times. The evaluation of the present status of the provinces affected by COVID-19 is very much essential to the government authorities to impose preventive strategies in controlling the spread of COVID-19 and to take necessary measures. In this article, a computational methodology is developed to estimate the present status of states and provinces which are affected due to COVID-19 using a fuzzy inference system. The factors such as population density, number of COVID-19 tests, confirmed cases of COVID-19, recovery rate, and mortality rate are considered as the input parameters of the proposed methodology. Considering positive and negative factors of the input parameters, the rule base is developed using triangular fuzzy numbers to capture uncertainties associated with the model. The application potentiality is validated by evaluating Pearson’s correlation coefficient. A sensitivity analysis is also performed to observe the changes of final output by varying the tolerance ranges of the inputs. The results of the proposed method show that some of the provinces have very poor performance in controlling the spread of COVID-19 in India. So, the government needs to take serious attention to deal with the pandemic situation of COVID-19 in those provinces.

COVID-19 的爆发已经显示出它对人类的有害影响，特别是对卫生部门、全球经济、教育系统、文化、政治和其他重要领域的影响。与全球大多数受影响的国家一样，印度最近因 COVID-19 而面临严重危机。评估受 COVID-19 影响的省份的现状对于政府当局实施预防策略以控制 COVID-19 的传播并采取必要措施非常重要。在本文中，开发了一种计算方法，使用模糊推理系统来估计受 COVID-19 影响的州和省的现状。人口密度、COVID-19 检测次数、COVID-19 确诊病例、康复率等因素，和死亡率被视为拟议方法的输入参数。考虑到输入参数的正面和负面因素，规则库是使用三角模糊数开发的，以捕获与模型相关的不确定性。通过评估皮尔逊相关系数来验证应用潜力。还执行灵敏度分析以通过改变输入的公差范围来观察最终输出的变化。所提出方法的结果表明，一些省份在控制 COVID-19 在印度的传播方面表现非常差。因此，政府需要认真对待这些省份的 COVID-19 大流行情况。考虑到输入参数的正面和负面因素，规则库是使用三角模糊数开发的，以捕获与模型相关的不确定性。通过评估皮尔逊相关系数来验证应用潜力。还执行灵敏度分析以通过改变输入的公差范围来观察最终输出的变化。所提出方法的结果表明，一些省份在控制 COVID-19 在印度的传播方面表现非常差。因此，政府需要认真对待这些省份的 COVID-19 大流行情况。考虑到输入参数的正面和负面因素，规则库是使用三角模糊数开发的，以捕获与模型相关的不确定性。通过评估皮尔逊相关系数来验证应用潜力。还执行灵敏度分析以通过改变输入的公差范围来观察最终输出的变化。所提出方法的结果表明，一些省份在控制 COVID-19 在印度的传播方面表现非常差。因此，政府需要认真对待这些省份的 COVID-19 大流行情况。通过评估皮尔逊相关系数来验证应用潜力。还执行灵敏度分析以通过改变输入的公差范围来观察最终输出的变化。所提出方法的结果表明，一些省份在控制 COVID-19 在印度的传播方面表现非常差。因此，政府需要认真对待这些省份的 COVID-19 大流行情况。通过评估皮尔逊相关系数来验证应用潜力。还执行灵敏度分析以通过改变输入的公差范围来观察最终输出的变化。所提出方法的结果表明，一些省份在控制 COVID-19 在印度的传播方面表现非常差。因此，政府需要认真对待这些省份的 COVID-19 大流行情况。